Project Summary The ability to understand and measure heterogeneity is necessary in order to make advances in fighting diseases like Alzheimer's, diabetes, and cancer. The ability to elucidate the differences between cell types and their locations within a tissue would provide insight into how cells interact with each other and the mechanisms that give rise to various disease states, and would help identify therapeutic pathways for new treatments. Specifically in cancer, intratumor heterogeneity is a phenomenon that actively leads to treatment failure and relapse in cancer patients. The limitations of current tools for understanding and measuring heterogeneity inhibit personalized medicine and the discovery of advanced therapeutics. Current methods are either 1) incapable of providing spatial data alongside `omics data or 2) have workflows too cumbersome to facilitate broad adoption. bioSyntagma has developed a method for resolving heterogeneity by correlating the spatial information in cellular imagery with multi-omic analysis. This workflow is easily automated and scalable through its microfluidic platform which images tissue, identifies cells of interest, and then isolates imaged cells for molecular analysis. We propose to utilize this technology to enable single-cell applications. SA1: Establish RNA quality after laser extraction of single cells for enrichment workflow: We will optimize tissue preparation protocols to maximize the quality of genetic material produced from the microfluidic device. This includes parameters of tissue preservation, staining for fluorescent targets, and laser manipulation of cells. SA2: Establish single-cell specificity during laser enrichment workflow: Isolating single cells on a microfluidic device after imaging requires precise manipulation with laser beams. We will demonstrate the ability to achieve single-cell specificity and optimize parameters for single-cell analysis. SA3: Demonstrate ability to detect transcriptomic heterogeneity within tumor samples: We will demonstrate the clinical utility of this technology by identifying heterogeneity within immune cells of a tumor based on their relative locations. This type of novel analysis would enable drug discovery and patient screening for personalized medicines. A successful outcome this proposal will be a method for spatially resolving single-cell molecular analysis with demonstrated utility in immuno-oncology biomarker discovery. Applications of the technology will extend beyond cancer into brain studies and other diseases. Further automation to increase throughput will be the subject of a future phase II submission in order to enable translation into a clinical environment.